基于改进新陈代谢GM(1,1)模型对钢管混凝土拱桥涂膜腐蚀预测

Forecasting for Protective Coating Corrosion of the Concrete-Filled Steel Tube Arch Bridge Based on the Improved Metabolic Model GM( 1,1)

目的提出改进新陈代谢GM(1,1)模型,提高预测钢结构使用寿命的精度.方法在全序列的基础上,置入一个由传统GM(1,1)模型得到新数据,去除一个旧的数据,建立既保证了原来的维数,而又不影响整个信息发展趋势的改进新陈代谢GM(1,1)模型.利用改进新陈代谢GM(1,1)模型对已经用传统GM(1,1)模型预测钢管混凝土拱桥涂膜腐蚀的实际工况进行重新预测,验证所提出的改进新陈代谢GM(1,1)模型在涂膜腐蚀预测中应用的可行性、有效性及预测所提高的精度.结果改进新陈代谢GM(1,1)模型的均值方差比值C为0.132 9,比传统GM(1,1)模型的均值方差比值C的值0.172 1小,改进新陈代谢GM(1,1)模型的精度比传统GM(1,1)模型的预测效果好;改进新陈代谢GM(1,1)模型的平均相对误差为3.20%,传统GM(1,1)为4.01%,提高了预测精度.结论改进新陈代谢GM(1,1)模型既保证了传统GM(1,1)模型的维数,而又不影响整个信息的发展趋势,改进新陈代谢GM(1,1)模型更合理,适用于中长期预测.

This paper proposes an improved metabolic model GM（ 1,1） in order to enhance forecasting precision for life of steel structures. Based on the complete sequence,using the newdatataken from the traditional GM（ 1,1） model to replace the old data,an improved metabolic GM（ 1,1） model is built,which not only ensures the original dimensionality,but also retains the growing trend of the whole information. Forecasting for coating corrosion in a concrete-filled steel tube arch bridge with the proposed model is carried out and compared with the traditional GM（ 1,1） model.It is found that the mean square error C with the improved model is 0. 132 9,which is much lower than the value 0. 172 1 obtained with the traditional GM（ 1,1） model. The relative mean error of the improved metabolic GM（ 1,1） model（ 3. 20%） is also smaller than that of the traditional metabolic GM（ 1,1） model（ 4. 01%）. Conclusion is that the improved metabolic GM（ 1,1） model has higher forecasting accuracy than the traditional one. It ensures the original dimensionality and retains the growing trend of the whole information,which means the improved metabolic model GM（ 1,1） is more reasonable and suitable for long term forecasting.